System and method for automating categorization and aggregation of content from network sites

ABSTRACT

A plurality of content items are retrieved from multiple network sites. Content from each content item is programmatically analyzed in order to associate that content item with one or more categories. The one or more categories may be part of a larger set of predefined categories. A network page is assigned to one or more corresponding categories in the set of predefined categories. At least some content is provided on the network page using one or more content items that were associated with the one or more categories assigned to that network page.

RELATED APPLICATIONS

This Application is a Divisional of U.S. patent application Ser. No.10/888,787, filed Jul. 9, 2004 now U.S. Pat. No. 8,271,495 which claimsbenefit of priority to U.S. Provisional Patent Application No.60/531,150, filed Dec. 17, 2003; all of the aforementioned priorityapplications being hereby incorporated by reference in their respectiveentirety for all purposes.

TECHNICAL FIELD

The disclosed embodiments relate generally to the field of contentprovided on network sites. More particularly, the disclosed embodimentsrelate to a system and method for automating categorization andaggregation of content from network sites.

BACKGROUND

With the growth of the Internet, web-sites are increasingly providingcontent such as news, articles, and stories. There are an increasingnumber of sources for content on the Internet. With this growth, contentdistribution on the Internet has become disorganized. For example,popular news sites carry redundant news items, so users have little needto visit more than one news source. For a user to receive comprehensivenews items of a given topic, such as their local area, the user may haveto visit numerous sites and materials. At the same time, a user may findit difficult to find a news item about an obscure category, such as adisease or a hobby. In such cases, users often rely on search sites,such as provided by YAHOO! or GOOGLE to locate content items ofinterest.

There are web-sites that categorize content for users, but in mostcases, the categories are fairly broad and non-specific. For example,the typical news site will provide aggregation of news stories underheadings such as World News, U.S. News, Sports, Business etc. Theaggregation and categorization of such stories is typically done throughsome manual intervention. A typical situation is that the story iscategorized in a general category at its origin, and then distributedfor consumption or display on multiple web-sites. Another situation isthat editors provide keywords in a story, or associate the keywords withthe stories, so that when someone types a search term at a search sitethat matches the key word, the story will be presented in the searchresult.

Some sites provide category-specific content by searching for contentthat matches a particular search term. Such sites typically rely on theuse of search terms to ensure that a particular content item issufficiently pertinent to a particular category. When content isidentified, it is known to belong to a category of the search term.

SUMMARY OF THE INVENTION

According to embodiments described herein, a plurality of content itemsare retrieved from multiple network sites. Content from each contentitem is programmatically analyzed in order to associate that contentitem with one or more categories. The one or more categories may be partof a larger set of predefined categories. A network page is assigned toone or more corresponding categories in the set of predefinedcategories. At least some content is provided on the network page usingone or more content items that were associated with the one or morecategories assigned to that network page.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for retrieving, categorizing and aggregatingcontent for display on a network, according to an embodiment.

FIG. 2 illustrates a basic method for automatically analyzing contentitems for categorical content, according to an embodiment.

FIG. 3 illustrates a method in which categorization of content items isperformed in order to aggregate and display content on network pagescorresponding to one or more categories, according to an embodiment.

FIG. 4 is a method illustrating automated retrieval, categorization,aggregation and display of content items, according to an embodiment.

FIG. 5 illustrates processes that form part of a programmatic analysisto categorize content items based on the item's text, according to anembodiment.

FIG. 6 is a block diagram of a system that produces formatted networkpages where content is aggregated based on categories, according to anembodiment.

FIG. 7 illustrates a method in which content from a second category issuggested on a formatted page where content is aggregated and displayedfor a first category.

FIG. 8 illustrates a formatted page for displaying content that isderived from categorized content items, according to an embodiment.

FIG. 9 displays a formatted page, according to another embodiment.

FIG. 10 illustrates a method for categorizing content based ongeographic information, under an embodiment of the invention.

In the drawings, the same reference numbers identify identical orsubstantially similar elements or acts. To easily identify thediscussion of any particular element or act, the most significant digitor digits in a reference number refer to the Figure number in which thatelement is first introduced. Any modifications necessary to the Figurescan be readily made by one skilled in the relevant art based on thedetailed description provided herein.

DETAILED DESCRIPTION

Overview

Embodiments of the invention describe a system and method forautomatically retrieving, categorizing and displaying content from anetwork. An embodiment of the invention enables category-specificcontent to appear together at one site or location on a network. Oneresult that may be achieved is that a user may access and browse thesite or location where category-specific content is aggregated andupdated.

In one application, a web page is provided that can be browsed by auser, where the web page includes content dedicated to a particularcategory. The content may include links to articles, news stories andother content items that are about the particular category. For example,the user can view a web page having updated news stories about aparticular hobby, disease, person of interest or company. These articlesand news stories may be retrieved from various other network sources,and presented on the page to maximize interest and reduce redundancy. Assuch, the user is provided with an alternative to having to submitsearch queries in order to view category-specific content items.

In an embodiment, a large number of content items may be retrieved andcategorized into an even larger number of categories throughprogrammatic implementations. This allows for content to be generatedfor various category-specific web pages (or portions thereof). Thecontent for each page may be retrieved automatically from variousnetwork sites.

One embodiment provides an automated process where content iscategorized, aggregated and selected for display on category specificpages. This enables the creation of category-specific web pages thatprovide fresh and pertinent content for a specific category. Readersinterested in a particular category may view a web page as a singlesource where information about the category of interest is provided. Anembodiment such as described may obtain content for such pages fromnumerous sources that most users would not have time to access manually.The user may not even have knowledge of all the different sources thatprovide content about that particular category at a given moment.

According to an embodiment, a plurality of content items are retrievedfrom multiple network sites. Content from each content item isprogrammatically analyzed in order to associate that content item withone or more categories. The one or more categories may be part of alarger set of predefined categories. A network page is assigned to oneor more corresponding categories in the set of predefined categories. Atleast some content is provided on the network page using one or morecontent items that were associated with the one or more categoriesassigned to that network page.

Examples of content items include news items and events, announcements,messages, press releases, product and pricing advertisements (or otherinformation), sale information (e.g. department store sale), pricingevents, and articles. In one embodiment, content items include textsegments that can be used to perform analysis operations describedherein. The term “content” may refer to reproductions or derivations ofcontent items, summaries, segments or portions of content items, and/orlinks to other network sites where the content items are provided.

Embodiments of the invention categorize content items into a selectedset of categories. The selected set of categories are from a much largernumber of possible categories. In one embodiment, the total number ofpossible categories in which news items pertain to is of the order of10³ or greater. A category may be broad, such as a genre (entertainment,business, news items), or specific (individual celebrities, professionalathletes, companies). Categories are identifiable by sub-categories(e.g. entertainment is defined by individual celebrities and movietitles) and/or by key words, phrases, or text-strings. However, as willbe described herein, the occurrence of a key word, phrase ortext-strings that is a category identifier may only trigger adetermination as to whether a particular content item containing thatidentifier should be associated with the category identified by thatidentifier.

An embodiment of the invention may be implemented on or with a networksuch as the Internet. For example, content items may correspond to newsstories, articles and other documents made available at any one of theplethora of web-sites where news and other content is provided.

The term “programmatically” means an automated step, or substantiallyautomated process performed through use of computer-executableinstructions, such as by processors which execute instructions in theform of programming code.

As used herein, the term “module” includes a program, a subroutine, aportion of a program, a software component, firmware, a hardwarecomponent, or a combination thereof, capable of performing a stated taskor function. A module can exist on a single machine, or be distributedto more than one machine.

Embodiments described herein may include instructions that are carriedon or executed by a computer-readable medium. As used herein, acomputer-readable medium may include any machine or device havingresources to execute, store, or otherwise carry instructions forperforming operations and steps of embodiments described herein. Modulesand software components described herein may be executed on one or moremachines and by one or more devices. Instructions for executing modulesand software components may be carried in memory mediums, eitherinternally or externally from machines on which instructions areexecuted.

According to another embodiment, a method is provided in which aplurality of content items are retrieved from one or more network sites.Content for each of the plurality of content items is analyzed in orderto associate that content item with one or more categories in a largerset of categories.

System Overview

FIG. 1 illustrates a system for retrieving, categorizing and aggregatingcontent for display on a network, according to an embodiment. The systemmay be comprised of a combination of modules or components thatcooperate with one another. A system such as described automates theacts of retrieving and sorting content items into categories through theuser of a combination that includes a crawler 110, a categorizer 120,and a knowledge database 130. The system may aggregate or select contentfor display based in part on the retrieved content through the use of abucket 140 and an editor 150. The system may operate on a network suchas the Internet.

A system such as described in FIG. 1 may be used to maintain numerouspages, and each of the pages may include categorized content that isaggregated and maintained in an updated state. Each page or document maydisplay aggregated content from various network sites based on one ormore specific categories assigned to that page. Each page may beroutinely and automatically updated using additional content aggregatedfrom any one of the numerous web sites that the system accesses. In oneembodiment, the pages on which the system maintains and provides contentare made available to users over the Internet.

Crawler 110 may be configured to visit pre-determined network siteswhere news stories and other content are periodically provided. Forexample, newspaper cites and cites that carry wire services for majornews organizations such as REUTERS, ASSOCIATED PRESS, NEW YORK TIMES,and BLOOMBERG may be periodically accessed. In addition, crawler 110 mayaccess local (geographic specific) news resources, journals, real-timeinformation providers (stock quotes from stock exchanges), webclippings, message boards, online retail sites (including sites wherepricing information for “brick and mortar” outlets are provided), or anyother site where content is provided and updated on occasions. Crawler110 may be configured to automatically provide registration informationfrom sites that require users to be registered. For example, crawler 110may enter login, password, or otherwise perform a script in order togain access to a web-site. In addition, crawler 110 may be configured tovisit individual sites at particular times, or at designated frequencyintervals. For example, crawler 110 may be programmed to visit differentnetwork sites at different intervals based on how frequently differentweb sites are known to refresh their own content.

In an embodiment, crawler 110 provides text-based content to categorizer120. Categorizer 120 works with knowledge database 130 to categorizecontent provided by crawler 110. In particular, categorizer 120 andknowledge database 130 may combine to determine one or more matchingcategories for a particular content item. In an embodiment, categorizer120 uses multi-dimension or multi-space algorithms in order to sortspecific content items into one or more of the categories defined in theknowledge database 130. Categorizer 120 may analyze text from thecontent items in order to find text-string combinations which matchspecific category definitions. Knowledge database 130 may store categorydefinitions (described in more detail with as nodes in FIG. 5) whichconsist of a set of text-string combinations that are identifiers of aparticular category. Identifiers may be of different degrees. Someidentifiers may be used to increase confidence, others to be moredeterminative. A more detailed explanation of how a category identifieris used is provided with FIG. 5.

A category identifier may be either one of a required or pertinent setof text-string combinations. As will be described, one embodimentprovides that the presence of one or more words, phrases, names or othertext-strings from the required set of a given category definitiontriggers the system into considering that category as a candidatecategory that matches the content item. The presence of additionalidentifiers, whether from the required or pertinent set, may beconsidered in a subsequent determination of whether the given categoryis a good match for the content item.

Thus, the occurrence of a single text-string that corresponds to acategory identifier is, by itself, often insufficient to match thecontent item of the text-string to the category of the identifier.Rather, the presence of the identifier in the content item marks acandidate category that is subsequently analyzed. Additional analysis isdone on the content item. According to one embodiment, for any givencandidate category, the additional analysis factors in the following:the number of identifiers (required and pertinent) in the content item,the commonality of the identifiers that are present, the placement ofthe identifiers in the content item, the relation of the identifierswith surrounding text, the character length of the identifiers, and ageneral measurement of how well individual identifiers identify acategory based on the size of the category definition and other factors.Other factors may also be used.

In one embodiment, knowledge database 130 contains a large number ofnodes, alternatively referred to as category identifiers. In oneapplication, the total number of nodes that can be maintained may exceedthe order of 10³. For example, in one specific application, the numberof nodes maintained by the knowledge database is of the order of 10⁶. Asystem such as described herein is capable of retrieving content itemsfrom various sources and categorizing content from the content itemsinto any one of the plethora of categories. One application for such anembodiment is a web-site that provides thousands, or tens of thousands(or more), of internal web-pages, each specific to one category, oralternatively to a small set of categories. In such an application, eachinternal web page is a site where category-specific content isaggregated, and possibly selected for display.

Past attempts to aggregate and categorize content for display on networksites have focused on using a combination of manual editing, and/or keyword queries to locate, categorize and select content for display. Suchattempts have been limited in their ability to categorize data intoanything but a small set of categories. For example, many news sitesthat pull news from other web sites, display news items in broadcategories, such as World News. Sports, Health, Business etc. Incontrast to such systems, embodiments described herein can, for example,host one page for each publicly traded company in a general Businesscategory, and on each company-specific page, news items for that companyare frequently retrieved and displayed. This gives the user the abilityto view fresh news items for one company at one site, rather than makingthe user sift through a broader general category for news that may ormay not be of interest. Websites such as google.com provide the userwith the option of searching news items based on a keyword query.However, such sites provide only search results for a user's query. Theuser still has to sift through the search results, which may or may notbe of interest. There may have been problems with the user's search(such as one of the keywords having two different meanings).Furthermore, the search results only locate stories with given keywords,the search results make no determination as to whether the story islikely to be of interest. In contrast, embodiments described hereinenable generation of web pages where content is category-specific andlikely to be of interest to someone who is interested in category of theweb page.

Crawler 110 may retrieve thousands of items, such as articles and newsstories, in a given interval of time (such as a day) using a largenumber of sources (such as web-sites where articles are published).Next, categorizer 120 scans text content from the content items in orderdetermine candidate categories. As stated, candidate categories mayrefer to each category that has an identifier in the text content of theitem. In one application, the scan of a given item yields tens orhundreds of candidate categories. Categorizer 120 makes a determinationfrom the candidate categories as to which categories are mostappropriate for a given content item using the algorithms (such asmulti-dimensional processes described with FIG. 5).

In determining what category matches a particular content item,categorizer 120 may make the following determinations, either absolutelyor in terms of probabilities: (1) associate a text-string with acandidate category; (2) determine whether the text string is in factreferring to the candidate category; and (3) if the text string isdetermined to refer to the candidate category, determine if thecandidate category the subject of the content in the content item (i.e.is the article about the candidate category?).

Knowledge database 130 may include information for use in analyzing theapplicability of a category identifier to a particular category. In oneembodiment, knowledge database 130 includes information for enabling thecategorizer 120 to make the first two determinations of the precedingparagraph. Specifically, knowledge database 130 may correlatetext-strings with categories, and also provide information in order todetermine whether the occurrence of the text-string implies the contentitem is in fact referring to the correlated category.

The information maintained by knowledge database 130 may includeinformation that indicates the commonality (or inversely the uniqueness)of particular category identifiers. Commonality and uniqueness arefactors which influence the confidence that the presence of a particularcategory identifier in the text of a content item in fact means that thecontent item is about the category of that category identifier. Forexample, knowledge database 130 may contain information from the BritishNational Corpus on how common (or unique) a particular word or phraseis. Similarly, the United States Census Bureau publishes the 5000 mostcommon first names, and the 35000 most common surnames. The commonalityof geographic places, such as city and street names, may be obtainedfrom sources such as RAND MCNALLY.

To provide one example, the appearance of text string “Bill Gates” mayidentify MICROSOFT and BILL GATES as candidate categories. But knowledgedatabase 130 will also factor in the possibility that “Bill Gates” maymean a different person, based on the U.S. Census Bureau informationindicting Bill and Gates are semi-common first names and surnames. Ifthe same article includes the word “windows”, the commonality of thatword may be determined by the British National Corpus. Thus, knowledgedatabase 130 may determine the likelihood that the article is referringto BILL GATES and MICROSOFT based on the commonality of the name and ofthe word “windows”. Information for determining commonality/uniquenessof words, names and phrases may enable categorizer to determine alikelihood that “Bill Gates of Topeka, Kans. was standing by his windowwhen he saw his neighbor's house burning,” is not a story about BillGates, founder of Microsoft.

It should be noted that even if occurrence of “Bill Gates” and “window”is deemed to be a likely reference to the more famous founder ofMICROSOFT, additional analysis is performed to determine if the articleis in fact about MICROSOFT or the famous founder of that company. Forexample, categorizer 120 may be configured to decipher that a story line“After winning the lottery, John Smith may just as will be Bill Gateswhen he invented Windows,” is a story that is not about the founder ofMICROSOFT. A more detailed description of how such determinations aremade is provided with embodiments described below.

Categorizer 130 outputs categorized content. Categorized contentincludes content from items that have been categorized into one or morecategories. In one embodiment, text from a content item is outputted andassigned to a small set of categories.

Bucket 140 groups categorized content. In one embodiment, categorizedcontent for each category is aggregated as it becomes available. Theoutput of bucket 140 includes content clusters, which refers to a set ofaggregated content for individual categories. The aggregated content mayinclude text from the original content item. In addition, graphics, suchas images, may be stored with the text content from the item. Some orall of the text from a particular content item may form the content fromthat item that is part of the set of aggregated content. It is alsopossible for the image or graphics originally provided with the contentitem to form part of the content from that item, and as such, be part ofthe set of aggregated content.

Aggregated content for each category is provided to a module referred aseditor 150. Editor 150 selects which of the aggregated content is to bedisplayed at a given interval on a corresponding network page of thatcategory. Editor 150 performs operations for generating displayedcontent from the aggregated content clusters. Editor 150 selects whatcontent is to appear on a network page using a set of selection criteriaor rules. According to one embodiment, bucket 140 uses content analysisof each item forming the aggregated content to determine when items inthe aggregated content are the same, or at least very similar. Editor150 selects content items from the bucket 140. One criteria that may beused by editor 150 to select items from the aggregate content is toexclude redundant content items from appearing on the page. For example,if two stories in the aggregated content each contain an identicalportion, the editor 150 may determine that only one of the two storiesneeds to appear on the page. Another rule or factor that may be used toselect a particular content item from the aggregated content is thesource of the content item. For example, some web sites may be preferredover other web sites as sources of news stories. Other examples offactors that can be used in selecting what content to display fromaggregated content items include key words or phrases and freshness.Additional factors that may be used include, location/source of contentitems, location of subject of content items, prominence of content item,geographic distance between a subject of the content item and thelocation of the readers, and geographic distance between subject ofcontent item and source of content item.

In one embodiment, aggregated content may individually be presented inthe form of short summaries, headlines, and sub-headlines, with links tothe entire content item. The link may be to the network site where thecontent item was originally retrieved from and analyzed.

Methodology

FIGS. 2-6 illustrate methods, according to embodiments of the invention.Embodiments such as described in FIGS. 2-6 may be performed though useof machines that can execute instructions stored on computer-readablemediums. Specifically, methods such as described in FIGS. 2-6 may beperformed by one or more processors, which execute instructions forperforming steps or operations of the methods described. A system suchas described in FIG. 1 is an example of a suitable system for performingmethods such as described below. Any reference to an element of FIG. 1is made solely for illustrative purposes.

FIG. 2 illustrates a basic method for automatically analyzing contentitems for categorical content. As described, a step 210 provides thatcontent is retrieved from different network sites. For example, contentmay be retrieved from different web-sites using a crawler 100. Examplesof network sites that can be used to retrieve content items includesweb-sites where articles such as news stories are provided. Otherexamples include sites where press releases, product listings,advertisements, events and other news worthy or content of interestitems are provided.

Step 220 provides that content items are programmatically analyzed inorder to determine which one of a predefined set of categories belong tothat item. For example, this step may be performed by categorizer 130using knowledge database 120 to analyze text from a news story. Theknowledge database 130 may contain information for defining a largenumber of categories. The text from the news story may be automaticallyscanned for text strings that identify candidate categories. A series ofanalysis tools may be used to determine which candidate categories arepotentially related to the content item.

In step 230, the analysis performed in step 220 is used to sort theitems retrieved in step 210 into one or more of the predefinedcategories. In one embodiment, the category or categories that areassigned to the content item are selected from the candidate categories.For example, one news article may generate hundreds of candidatecategories. Of the candidates, a programmatic determination is made todetermine which categories are most appropriate for a given contentitem. The content item is assigned to one or more categories that aredeemed appropriate based on criteria and ruled for determining whichcandidate categories are most relevant or accurate in identifying thebest category for a particular content item.

FIG. 3 illustrates a method in which categorization of content items isperformed in order to aggregate and display content on network pagescorresponding to one or more categories, according to one embodiment ofthe invention.

Step 310 provides that content items, such as articles, news storiesetc, are retrieved from different web sites (assuming use of a networksuch as the Internet).

In step 320, the content items are scanned in order to identify categoryidentifiers. In one embodiment, text content of the content items isscanned. An attempt is made to find as many category identifiers aspossible in the text content.

Step 330 provides that an analysis is performed of the categoryidentifiers identified from the scan of the content item. A moredetailed discussion of the analysis performed on the categoryidentifiers is provided with FIG. 5. The analysis is performed toidentify which categories should be assumed as being most relevant tothe particular content item.

In step 340, an aggregation of content items is made available for aparticular category. The aggregation may be made available visually on apage that is accessible to others over a network (such as the Internet).The aggregation of the content items may be in the form of a summariesor edited versions of the content items appearing on the page togetherLinks to network sites where the content items are actually provided mayalso be included as, or part of, the aggregated content.

FIG. 4 is a method illustrating automated retrieval, categorization,aggregation and display of content items. In step 410, categories aredefined by one or more identifiers. A category definition may include aset of names, words, phrases, geographic locations or other textstrings. For example, the category definition for a celebrity mayinclude the celebrity first name, last name, nickname, film biography,and possibly the place of residence or birth for the celebrity. Thecategory definition for a location may include the name of the place,the name of geographic identifiers of the location, longitude andlatitude of the location, historical names and nicknames for thelocation, the names of parks, bodies of water, tunnels, rivers, schoolsjails, businesses (restaurants etc), and any other information that isindicative of that location.

In step 420, articles (or other content items) are automaticallyretrieved from multiple network sites. For example as discussed withother embodiments, web sites where news items, articles, messages etc.may be routinely accessed, and content appearing thereon may beretrieved.

Step 430 provides that the content of the articles are scanned, orotherwise inspected for identifiers of categories in order to identifycandidate categories. In one embodiment, text is scanned for names,words, phrases, geographic locations and other text strings thatcorrespond to identifiers of categories. A candidate category means thatan identifier of that category appears in the article, but otheranalysis needs to be performed in order to be able to conclude that thearticle belongs in that category.

In step 440, an analysis is done to determine which candidate categoryor categories is a suitable categorical match for the particulararticle. A more detailed explanation of the process for performing theanalysis is described with FIG. 5. The result of performing the analysisof this step is that the article is assigned to one or more categories.

In step 450, articles matching a particular category are aggregated. Inthe case where a category is specific (such as a specific celebrity orathlete), the rate at which articles are accumulated may be relativelyslow. For categories that match genre's (such as entertainment andsports), the rate of accumulation may be fairly quick. In many cases,there may be too many articles to be displayed on one screen or networkpage.

In step 460, articles from the set of aggregated articles are selectedto be displayed or otherwise rendered in a medium that is specific tothe category of the articles. This step may be performed in order toselect what articles are made available on a network page, placement ofarticles or links to articles on a page, and what portion or eveninformation is displayed about selected articles on the page. Theselection process may be based on several factors. In one embodiment,these factors include (1) how recent article was published, (2) amountof interest in the article from the public (information may be obtainedfrom the source or from the subject matter or identifiers in thearticle) (3) the degree to which a particular article varies from otherarticles that have been aggregated for the network page (e.g. does thearticle share the same identifiers as other articles for the samecategory), (4) the degree of confidence that exists in the determinationthat the article belongs in the category, (5) how geographically closethe content items are to the subject of the content items; (6) thegeographic distance between a location of the content item and alocation of the reader, (7) prominence of the source of the contentitems (e.g. national newspaper), and (8) how often the source of thecontent item reports about a particular subject. With respect to (8), anexample is a publication that is authoritative for a particular topic.For example, an automotive racing magazine is more authoritative about arace car driver or racing story than a local news paper. Therefore, inthe example provided, one embodiment may provide more weight to newsstories identified as belong to an automobile racing category when thenews stories originates from the more authoritative source (themagazine).

Categorization

As described above, embodiments of the invention provide for automaticcategorization of content retrieved from different network sites. In oneembodiment, text content in different articles is retrieved and scannedfor category identifiers, which may be in the form of words, phrases,names or locations. For each category identifier in a given article,additional analysis is performed in order to determine whether anarticle is about or otherwise belongs in a category.

FIG. 5 illustrates a programmatic analysis performed on text content510. An analysis such as described herein may be performed by a systemsuch as described in FIG. 1. Reference to elements of FIG. 1 are madefor illustrative purposes only. In an embodiment, text content 510corresponds to content that is read from an article on a network site.The results of the overall analysis is a determination of an appropriatecategory for the text content 510. FIG. 5 shows results of severalindependent processes performed as part of the overall analysis forassigning the article to a category. Each category may be represented bya node. A node may defined by a set of identifiers, which include words,phrases, names and other text-strings. In one embodiment, each nodeincludes, as identifiers, one or more of (i) required term(s) and (ii)pertinent term(s). A required term may correspond to a categoryidentifier that is fairly unique to a particular category. The existenceof a required term in text invokes the category of that required term asa candidate. In one embodiment, a node may have one or more (evenseveral or hundreds) of required terms. One embodiment provides for thenode to be a candidate for a particular category, at least one of therequired terms has to be present in the text content.

For example, the full name of a celebrity, together in one text string,is an example of a required term for that celebrity. A common nicknameused to identify that celebrity (e.g. “Madonna” or “Prince”) may alsocorrespond to a required term for a celebrity. The pertinent term is aterm that is more common to multiple nodes. For example, the term“Corvette” may be a pertinent term for the artist “Prince”, andspecifically to a song by the artist, but “Corvette” itself could be areference to car model. Thus, support terms are used to build confidencethat the candidate node is actually being referenced, and even is thesubject matter of the text content.

According to one embodiment, the existence of required terms and supportterms is used to quantify a likelihood that (i) a given article is infact referencing the category of the node, and (2) the category beingreferenced is a subject of the article, so much so that the articleshould be assigned to that category. A more detailed description of thequantitative analysis is provided below.

According to one embodiment, knowledge database 130 may store nodedefinitions, including required terms and support terms for each node.The categorizer 120 may perform individual processes of the overallanalysis in determining when a node matches an article. Thedetermination that a node matches an article may be made automatically,through programmatic means, such through instructions executed bycategorizer 120.

A node may be invoked as a candidate if one of the required terms forthat node appears in the text content. Thus, each candidate node incolumn 514 has at least one required term from text content 510. Thecolumn 546 lists at least one of the required terms that appear in thetext content 510 for a candidate node. For example, in column 546, thephrase “Patent and Trademark Office” is an identifier (a required term)for the node “law/patent-trademark”. To further the example, thepresence of the name “Lee” is a required term for the node“city/durham-nh” and “city/lee-fl”.

A column 514 lists nodes by name or node identification. Prior tocompletion of the analysis, all listed nodes are candidates. In theexample provided, only one node is a matching node for the particulartext item. This node is indicated in a separate row 540. Variousparameters are determined about each mode in order to determine whethera particular node is a matching node for the particular text item. Acolumn 512 lists a binary parameter that is assigned a value based on adetermination of whether the category of that row is a subject of thatarticle. For this parameters, the value of “1” indicates that node is asubject of the article (alternatively phrased, the article is about thecategory of the node). The value “0” indicates that the article is notabout the category of the node. For the node to be a matching node, thevalue of the column 512 would need to indicate that the article issufficiently about the category of the node to warrant a positive value.The determination of the value of column 512 may be made based on thevalue of the other parameters.

Column 516 lists a Fail Parameter for each candidate node. The FailParameter is a Boolean determination as to whether the candidate node isactually being referenced. It indicates whether reference to therequired term of a given candidate node is an accurate semanticreference. For example, in the example provided, “Stephen, Minn.” isbeing referenced as a city because the article quotes a person named“Stephen”. Even though “Stephen” is a required term for “Stephen,Minn.”, the article is not actually referencing the town. Thus, the node“Stephen, Minn.” is assigned a negative Fail Parameter, as the referenceto the required term of that node is not accurate.

The determination of Fail Parameter is based on a commonalitydetermination. Factors that affect the commonality determination includethe commonality/uniqueness of the required term, as well the length ofthe string for the required term. Short and common required termsindicate a negative Fail Parameter, while, long and unique stringsindicate a positive value. In the example, “Patent and Trademark Office”is an example of both a long and unique string, while the string “Lee”is an example of a short, non-unique identifier that yields a negativeresult. A positive Fail Parameter result increases the confidence that anode is a matching node.

Column 518 lists the Score Parameter for each candidate node. The ScoreParameter is another confidence rating that the reference to therequired term is semantically accurate. This Score Parameter may bebased on commonality of the required term, as well as other factors.

Column 520 and 522 provide Group Hits and Total Hits parameters. Eachrequired term may be part of a group of terms that are equivalent insemantics, but different in syntax. For example, the locations “Mt.Lebanon. Penn.” and “Mount Lebanon, Pa.” are semantically equivalentreferences to the same city. The parameter Group Hits measures thenumber of hits an entire group of required terms receives. Depending onuse and learning algorithms, there may be a difference between 3 hits toone group, and 3 hits to three groups. The Total Hits parameter measureshow many total hits of identifiers (required terms and supplementalterms) are in the text content 510 for a given candidate node.

Column 524 lists the parameter “Number of Occurrences” for eachcandidate node. The Number of Occurrences counts the number of times therequired terms of the candidate node appear in the text content 510.

Column 526 lists the parameter “Position”. The Position parameter is ameasurement of proximity between the start of the article and the firstrequired term of the candidate node. Confidence is increased when arequired term is close to the start of the article. One exception isthat a geographic node may contain a required term at or near the end ofthe article.

Column 528 is a Boolean parameter “BadState”. The BadState parameter isan indication that there is a bias towards a candidate not being amatching node, where the indication is based on geographical data in thecontent item.

Column 530 indicates a value for the parameter “Node Size”. Thisparameter is a measurement of the number of required terms and pertinentterms in a particular geographic node. In the event that two geographicnodes are equally suitable matching nodes for a given article, thisparameter assumes the node with the most required terms is the morepopular, and thus more likely the subject of the given article. Forexample, “New York City” may have numerous required terms and pertinentterms, including “York”, “Big Apple” and “Empire State Building”. TheNode Size parameter may be used to distinguish an article as being aboutor more pertinent to New York City, as opposed to York, Pa.

Column 532 lists the parameter “Words”. This is a count of the number ofwords for the required term of the candidate node that appears in thetext content 510.

Column 534 lists the parameter “Length”. This is a count of the numberof characters for the required term of the candidate node that appearsin the text content 510. With both the Words and Length parameters, thegreater the value, the more unique the required term that appears in thearticle. Consequently, the greater the value of the Fail parameter, andthe more likely that the candidate node is a matching node.

Column 536 lists the parameter “Post”. This parameter measures thenumber of nodes in the knowledge database 130 which list the requiredterm as part of a longer string of characters as a required term. Forexample, the required term “San” will produce a large value because ofvarious cities and streets that start with the three letters. The higherthe value, the less likely the candidate node is a matching node.

The column 538 provides the parameter “Node”. This node is similar tocolumn 536, in that it measures the number of nodes that contain therequired term of that candidate node. As with the Post parameter, thegreater this value, the less likely that the candidate node is amatching node.

The column 540 lists the parameter “Frequency”. It measures the numberof times that the required term appears as any part of any identifierfor any node.

The column 542 provides the parameter “Short”. The Short Parameterindicates a probability that the required term of the candidate nodeappears in the text content as part of a proper noun. Words immediatelybefore and after each required term may be inspected for capitalizationin determining this Boolean value. For example, if the required term iscapitalized, not at the beginning of a sentence, and preceded orfollowed by another capital letter, the Short Parameter may indicatethat the required term is part of a proper noun. For example, in theexample provided, “Stephen” is shown as a proper noun, as it is followedby “Kunin”

Column 544 lists the parameter “Multi”. This parameter is a combinationvalue of one or more preceding values. For example, it may be asummation or average of two or more preceding parameters. The lower thisnumber, the more likely that the candidate node is a matching node.

The parameters in columns 532-544 indicate processes performed onrequired terms of candidate nodes. The same processes indicated by theparameters in columns 532-544 may be performed on support terms of eachcandidate node. That is, text content 510 may be scanned for supportterms of each candidate node. For identified support terms, the WordParameter, Length Parameter, Post Parameter etc. are determined. Ingeneral, analysis for support terms provide confidence for a candidateterm, but are not determinative.

A learning algorithm may be implemented in order to train a system touse the various parameters to match categories to articles. The systemmay be trained to weight parameters, determine overall scores, and drawconclusions for determining when candidate nodes are matching nodes. Inone embodiment, a learning process is conducted where each matching nodeof an article is manually inspected to determine whether the article andnode are a good match. When bad matches are found, a system such asdescribed in FIG. 1 is trained to identify a bad match when acombination of parameters in the future yield worst values on eachdimension. The manner in which support terms influence analysis ofrequired terms may also be toned with experimentation and learningprocesses. With use of a learning mode and implementation, a set ofrules may be developed that instructs a system on how to treat theoccurrence of given values, or conditions, when analyzing the content.

Displaying Categorized Content

According to one embodiment of the invention, categorized content isaggregated on separate network pages, sites, or page portions, and thenmade available to users over a network such as the Internet. FIG. 6illustrates a system where aggregated content can be displayed on anetwork page.

FIG. 6 is a block diagram of a system that produces formatted networkpages where aggregated content is provided based on categories. In oneapplication, a system manages content for a plethora of network pages,and each of the network pages provides selected (when possible)aggregated content for a particular category. A system such as describedin FIG. 6 may be substantially automated.

FIG. 6 illustrates a content item 604 that retrieved from a networksite, such as a web site where content is provided and updated. In anexample provided, the content item 604 is in the form of a news story,with text content and an image. To further illustrate, the text contentmay include a headline and/or by line.

A categorization process 610 performs an analysis such as described withFIG. 5 in order to associate or assign the item 604 to a particularcategory. Once the item 604 is assigned to the category, the itembecomes aggregated with other items. Thus, there may be several itemsthat are assigned to the same category. In many cases, there may be toomany items assigned to the same category, in that there is not enoughdesirable space of time to display every article on the network page.Details for categorizing and aggregating content items are describedwith previous embodiments.

In an embodiment, once item 604 is aggregated with other items of acommon category, a selection process 620 is performed. Suring theselection process, a determination is made as to whether the item 604should be displayed on the network page over other items. The selectionprocess 620 may be performed using some or all of the criteria listed inFIG. 4.

If item 604 is selected for display, a display process 630 is performedin order to configure and format the item 604 for display on a formattednetwork page 640. If the content item was originally displayed on itsnetwork site with an image, display process 630 may store and retrievethat image for display on the formatted page 640. Display process 630may also execute different sets of rules for formatting and configuringcontent from the item 604 on to the network page 640. In one embodiment,display process 630 may use a set of editorial rules 634 to conformcontent from item 604 to standard journalism editing rules. For example,if a person is provided in the image that is to be presented with thetext on the formatted page, the image is positioned so that the personis facing inward. Another editorial rule (based on journalism standards)is that a headline should not exceed ten words. Thus, if there is aheadline that exceeds this number on the original site, the displayprocess 630 may, through implementation of the editorial rules 634,replace or truncate the headline. A complete list of suitable rules forconforming to journalism standards and guidelines may be found in “TheAssociated Press Stylebook and Libel Manual,” Norm Goldstein, Editor.

The display process 630 may also use a set of display rules 638 toformat content from item 604. For example, the appearance, font andportion of the content from item 604 may be determined from the set ofdisplay rules 638. Display rules may provide how often content isupdated on certain portions of the category page. For example, withreference to FIG. 9, content in column 910 may be updated faster thancontent on column 920. Furthermore, the two columns may display contentaccording to different formats (e.g. size).

The result of the display process 630 is the formatted network page 640.The content appearing on the formatted page 640 may be updatedautomatically continuously, or repeatedly over the course of a giventime period. Furthermore, it is possible for content appearing on theformatted page 640 to originate from numerous sources on networks suchas the World Wide Web, because categorization, aggregation and selectionof the content items is done automatically. Without manual editing, alarge number of network sites can be checked for articles, news itemsetc. pertaining to a specific category of the network page. Furthermore,the large number of resources can be updated more rapidly. In oneapplication, the result is a network page that contains fresh contentpertinent to a very specific subject and from numerous sources on theInternet.

Displaying Associated Categories with Categorized Content

The use of categorization processes to categorize and aggregate contenthas several applications. Among these applications, it is possible toindicate suggested content to the reader of a content item, where thesuggested content is independent in subject matter from the contentbeing viewed.

In one embodiment, the suggested content is determined from the contentof the item being viewed. FIG. 7 illustrates a method in which contentfrom a second category is suggested on a formatted page where content isaggregated and displayed for a first category. In step 710, acategorization process is performed on an article (or other contentitem) where two or more matching nodes are identified and associatedwith the article (see description accompanying FIG. 5).

Step 720 provides that the article is displayed on a network sitededicated or otherwise associated with one of the categories identifiedfor that article. With reference to an embodiment such as described inFIG. 6, the content may be displayed on a formatted page 640, belongingto a first category.

Step 730 provides that one or more visual indications (such ashyperlinks) are provided of a suggested category matching a secondmatching node for the displayed article. In the case where hyperlinksare used, the links may be to network sites where content is aggregatedfor the suggested category. As an alternative, the suggested content mayyield an advertisement link, or display advertisement information.

An embodiment such as described in FIG. 7 can be implemented through useof categorization and display processes described with previousembodiments. Specifically, the ability to identify categories throughprocesses such as described in FIG. 5 enables the determination ofsecond categories. When content items are displayed on, for example, agiven page of a category, display process 630 (see FIG. 6) may providethe visual indication of the second category or categories. The visualindication may be in the form of a link, summary, suggested heading,advertisement, or other data structure.

Formatted Pages

FIG. 8 illustrates a formatted page 800 for displaying content that isderived from categorized content items, according to one embodiment. Aformatted page may correspond to an output from embodiments describedabove, such as formatted page 640 described in FIG. 6. In addition, anembodiment described with FIG. 8 assumes that content is derived fromarticles categories through processes and methods described in previousembodiments.

With reference to FIG. 8, a first content item 810 corresponds to asegment of an article. The article may originate from a first networksite. Included in the first content item 810 is an image 812, and textsection 814. Selection of a heading or other link may display all of thetext provided by the original article that appeared at the first networksite. The image 812 may be stored from the article that was the sourceof the content item. In one embodiment, the text segment 814 includesthe headline, sub-headline, and first few sentences of the text portionof the original article. A first link 815 may be provided to a secondpage for another category. The other category may be identified from thetext of first content item 810.

Similarly, page 800 may also display second content item 820 and thirdcontent item 830. Second content item 820 may include second link 825 toa category identified by a categorization process performed on the textcontent of that item. Likewise, third content item 830 may include thirdlink 835 to a third category identified by a categorization processperformed on the text content of that item.

In an embodiment such as shown, formatted network page 800 has a uniformresource locator (URL) 805 or other address that is indicative of thecategory of that page. For example, page 800 may be assigned to“category A”, and the content items 810-830 are selected by beingpertinent to that category. A portion of the URL 805 also includes theterm “category A”.

FIG. 9 displays a formatted page 900 according to another embodiment. InFIG. 9, a page of a given category (or set of categories) is segmented,and each segment provides content through a different set ofaggregation, edit and/or display rules.

In an example provided, formatted page 900 is provided with fourcolumns. The page itself may be associated with a particular category,and a URL 905 to the page may indicate that category. A title of thecategory for the formatted page 902 may be provided in a prominentposition. One or more of the columns display content from content itemsthat were categorized and aggregated. In an example shown, a firstprimary column 910 displays category specific content, identifiedthrough a categorization process such as described above. A secondprimary column displays content that may be category specific for thatcategory (or of another category), or non-category specific (e.g. topnews). A left column 930 may display advertisement links, and a rightcolumn displays category links 940, although either left or right columnmay display advertisement, category or combinations of links. The links,as well as any other content appearing on the left or right column 930,940 may be category specific as well, or independent of anycategorization process.

In one embodiment, different display configurations and/or rules areused to display content on at least two of the columns. For example,first primary column 910 may display news of a first category (e.g.local news), and second primary column 920 may display news of a secondcategory (e.g. national and world news). One of the columns may berefreshed using an automated categorization process, such as describedabove. For example, a system such as described in FIG. 1 may be used toidentify, aggregate and select content for that column. In addition,first primary column 910 and second primary column 920 may refresh atdifferent rates, or have different display rules. In one application,important news such as world headlines (“Big News”) appears on thesecond primary column 920, while specific or categorized content appearson the first primary column 910. The Big News may be more important, andrequire less updating, as such news has long news cycles. On the otherhand, category specific news may be refreshed more quickly, so thatrepeat visits to the page 900 is more likely to ensure fresh content forthe viewer.

One manner in which the category specific web-pages may be provided to auser is through use of a search function. The search function may act asa prompt. A user may enter a search term, such as for example, acelebrity name, or the name of a disease. The search term may correspondto a web page displaying category-specific content. The search resultmay be the formatted web-page corresponding to the search result. Thatpage may display updated content that is specific to the category of thesearch term.

Search Specific Categories

In an embodiment, categories may be generated, or re-configured fromexisting categories, based on information entered by or determined froma user. FIG. 10 illustrates an embodiment in which a category page ofcontent items may be generated or configured based on such information.One specific type of information that may be used to generate such apage is geographic location specified by the user. For example, a usermay utilize a service such as described in FIG. 10 to research or reviewcontent (e.g. local news) about the user's home destination, or anintended vacation destination.

In step 1010, content items located by crawler 110 are associated withgeographic information. This step may be done on an ongoing basis withthe aggregation of the content items. The search information may beselected so as to enable subsequent retrieval of content itemsresponsive to user information that matches the search information.Examples of geographic information that can be stored for each contentitem include longitude, latitude, and/or zip code. The content items maybe scanned for geographic information, using techniques such asdescribed above, in order to associate the content items with a specificgeographic information item. For example, a location of a source of thecontent item and/or of the subject of the content item may be identifiedand associated with that content item.

In step 1020, geographic information for use in a search is receivedfrom the user. In the example provided above, the information maycorrespond to known geographic or location information about the user.For example, the user may enter his zip code, or exhibit actionsindicating the user's geographic location. The geographic informationmay correspond to the longitude, latitude, street address, city or zipcode of the user. The information may be determined from the user,either directly or indirectly. For example, the user's terminal mayinclude cookies that identify the user's zip code or location.Alternatively, the information may be entered by the user as input, suchas through a search interface.

In step 1030, content items are selected for the user based on thegeographic information specified by the user. While embodimentsdescribed above provide for displaying categories to the user based onthe search term, another embodiment may provide for reconfiguring one ormore categories that match the search result to be location specific.Still further, embodiment provides for identifying on-the-fly a set ofcontent items based on the geographic information specified by the user.

Responsive to receiving the geographic information item, step 1040provides that the selected content items are presented to the user. Inone embodiment, selected content items are sorted by an approximatedistance from the user. For example, for cases when content itemscorrespond to news, news stories in the user's town are prominentlydisplayed, while news stories in an adjacent metropolis or the user'sstate or less prominently displayed. Still further, the order in whichthe news stories are presented to the user may be based on a distance ofthe geographic location stored with the particular news story and thelocation detected for the user.

To provide an example of an embodiment such as described in FIG. 10,user-input, past online activities (as tracked by cookies or other data)may be used to determine a location of the user. The location may bedetermined as longitude and latitude. When the user enters geographicinput, content items are identified that match the user's location. Thismay include content items that are determined to be sufficientlyproximate to the user (e.g. within 50 miles or in the same county).These content items are then included or otherwise provided for in apage or presentation displayed to the user.

As another example, the user may enter a zip code corresponding to hissuburb. In this example, content items may be selected which match thezip code, and which match surrounding suburbs as well as the majormetropolis of the locality. The page presented to the user may beconfigured to show the news stories (or other content items) of thatperson's specific suburb first. The remainder of the selected contentitems may be presented based on a distance of the subject or location ofthe content item from the user. For example, news stories of adjacentand/or most proximate suburbs may be displayed first, followed by themetropolis region, which may be further away than the surroundingsuburbs. Thus the order of presentation for a list of content itemsprovided on a page may be determined by the distance of the locations ofthose content items (e.g. subject or location of news story) from theknown location of the user.

Conclusion

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What we claim is:
 1. A system for aggregating content on a network, thesystem comprising: one or more processors to provide: a categorizationprocess that is configured to (i) analyze a text content of each of aplurality of content items that are provided at a plurality of networksites, (ii) determine one or more categories that are to be associatedwith each of the plurality of content items, (iii) associate each of oneor more of the plurality of categories with multiple content items; aselection process that is configured to identify a selection of some ofthe multiple content items that are associated with at least some of theplurality of categories; and a display process that is configured togenerate a web page for each of the plurality of categories, the webpage of each of the plurality of categories including at least a portionof each of multiple content items that are associated with that categoryby the categorization process, wherein for the web page of each of theplurality of categories, the portion of each of the multiple contentitems corresponds to (i) one of a phrase, sentence, or summary of thatcontent item, and (ii) a link to a source for that phrase, sentence orsummary.
 2. The system of claim 1, wherein the link is provided in thephrase, sentence or summary of that content item.
 3. The system of claim1, wherein the display process identifies the source as a newsaggregation web page.
 4. The system of claim 1, wherein the displayprocess displays the phrase, sentence, or summary of individual contentsin a separate segment of the web page.
 5. The system of claim 4, whereinthe display process formats each web page according to one or moreeditorial rules.
 6. The system of claim 5, wherein the display processselects an image for one or more items displayed on one or more of theweb pages, and wherein the image is selected or formatted for displaybased on the one or more editorial rules.
 7. The system of claim 4,wherein the categorization process is configured to determine one ormore categories that are to be associated with each of the plurality ofcontent items, wherein the one or more categories include anyone of aplurality of categories that for which at least some of individuallycorrespond to a geographic location.
 8. The system of claim 4, whereinthe categorization process is configured to determine one or morecategories that are to be associated with each of the plurality ofcontent items, wherein the one or more categories include anyone of aplurality of categories that for which at least some of individuallycorrespond to a name of a person or a place.
 9. The system of claim 4,wherein the categorization process is configured to determine one ormore categories that are to be associated with each of the plurality ofcontent items, wherein the one or more categories include any one of aplurality of categories that for which at least some of individuallycorrespond to a current event topic.
 10. A non-transitorycomputer-readable medium that stores instructions that, when executed byone or more processors, causes the one or more processors to: using theone or more processors to perform steps comprising: analyzing a textcontent of each of a plurality of content items that are provided at aplurality of network sites; determining one or more categories that areto be associated with each of the plurality of content items;associating each of one or more of the plurality of categories withmultiple content items; identifying a selection of some of the multiplecontent items that are associated with at least some of the plurality ofcategories; and generating a web page for each of the plurality ofcategories, the web page of each of the plurality of categoriesincluding at least a portion of each of multiple content items that areassociated with that category, wherein for the web page of each of theplurality of categories, the portion of each of the multiple contentitems corresponds to (i) one of a phrase, sentence, or summary of thatcontent item, and (ii) a link to a source for that phrase, sentence orsummary.
 11. The computer-readable medium of claim 10, wherein the linkis provided in the phrase, sentence or summary of that content item. 12.The computer-readable medium of claim 10, wherein the computer-readablemedium further stores instructions to cause the one or more processorsto identify the source as a news aggregation web page.
 13. Thecomputer-readable medium of claim 10, wherein the computer-readablemedium further stores instructions to cause the one or more processorsto display the phrase, sentence, or summary of individual contents in aseparate segment of the web page.
 14. The computer-readable medium ofclaim 13, wherein the computer-readable medium further storesinstructions to cause the one or more processors to format each web pageaccording to one or more editorial rules.
 15. The computer-readablemedium of claim 14, wherein the computer-readable medium further storesinstructions to cause the one or more processors to select an image forone or more items displayed on one or more of the web pages, and whereinthe image is selected or formatted for display based on the one or moreeditorial rules.
 16. The computer-readable medium of claim 13, whereinthe computer-readable medium further stores instructions to cause theone or more processors to determine one or more categories that are tobe associated with each of the plurality of content items, wherein theone or more categories include anyone of a plurality of categories thatfor which at least some of individually correspond to a geographiclocation.
 17. The computer-readable medium of claim 13, wherein thecomputer-readable medium further stores instructions to cause the one ormore processors to determine one or more categories that are to beassociated with each of the plurality of content items, wherein the oneor more categories include anyone of a plurality of categories that forwhich at least some of individually correspond to a name of a person ora place.
 18. The computer-readable medium of claim 13, wherein thecomputer-readable medium further stores instructions to cause the one ormore processors to determine one or more categories that are to beassociated with each of the plurality of content items, wherein the oneor more categories include any one of a plurality of categories that forwhich at least some of individually correspond to a current event topic.19. A computer-implemented method for generating a web page, the methodbeing implemented by one or more processors and comprising: using theone or more processors to perform steps comprising: analyzing a textcontent of each of a plurality of content items that are provided at aplurality of network sites; determining one or more categories that areto be associated with each of the plurality of content items;associating each of one or more of the plurality of categories withmultiple content items; identifying a selection of some of the multiplecontent items that are associated with at least some of the plurality ofcategories; and generating a web page for each of the plurality ofcategories, the web page of each of the plurality of categoriesincluding at least a portion of each of multiple content items that areassociated with that category, wherein for the web page of each of theplurality of categories, the portion of each of the multiple contentitems corresponds to (i) one of a phrase, sentence, or summary of thatcontent item, and (ii) a link to a source for that phrase, sentence orsummary.
 20. The method of claim 19, wherein the link is provided in thephrase, sentence or summary of that content item.